Unbiased K-mer Analysis Reveals Changes in Copy Number of Highly Repetitive Sequences During Maize Domestication and Improvement

K-mer-based Approaches to Bridging Pangenomics and Population Genetics

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes can be challenging for many species, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a very useful but underutilized tool for bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π=0.025 (R2=0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π=0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer-based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers.

k-mer approaches for biodiversity genomics

The wide array of currently available genomes displays a wonderful diversity in size, composition, and structure and is quickly expanding thanks to several global biodiversity genomics initiatives. However, sequencing of genomes, even with the latest technologies, can still be challenging for both technical (e.g., small physical size, contaminated samples, or access to appropriate sequencing platforms) and biological reasons (e.g., germline-restricted DNA, variable ploidy levels, sex chromosomes, or very large genomes). In recent years, k-mer-based techniques have become popular to overcome some of these challenges. They are based on the simple process of dividing the analyzed sequences (e.g., raw reads or genomes) into a set of subsequences of length k, called k-mers, and then analyzing the frequency or sequences of those k-mers. Analyses based on k-mers allow for a rapid and intuitive assessment of complex sequencing data sets. Here, we provide a comprehensive review to the theoretical properties and practical applications of k-mers in biodiversity genomics with a special focus on genome modeling.

A survey of k-mer methods and applications in bioinformatics

The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.

Early Detection of Both Pyrenophora teres f. teres and f. maculata in Asymptomatic Barley Leaves Using Digital Droplet PCR (ddPCR)

Efficient early pathogen detection, before symptom apparition, is crucial for optimizing disease management. In barley, the fungal pathogen Pyrenophora teres is the causative agent of net blotch disease, which exists in two forms: P. teres f. sp. teres (Ptt), causing net-form of net blotch (NTNB), and P. teres f. sp. maculata (Ptm), responsible for spot-form of net blotch (STNB). In this study, we developed primers and a TaqMan probe to detect both Ptt and Ptm. A comprehensive k-mer based analysis was performed across a collection of P. teres genomes to identify the conserved regions that had potential as universal genetic markers. These regions were then analyzed for their prevalence and copy number across diverse Moroccan P. teres strains, using both a k-mer analysis for sequence identification and a phylogenetic assessment to establish genetic relatedness. The designed primer-probe set was successfully validated through qPCR, and early disease detection, prior to symptom development, was achieved using ddPCR. The k-mer analysis performed across the available P. teres genomes suggests the potential for these sequences to serve as universal markers for P. teres, transcending environmental variations.

Trait association and prediction through integrative k-mer analysis

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

SPRISS: approximating frequent k-mers by sampling reads, and applications

MOTIVATION

The extraction of k-mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the characterization of RNA-seq datasets. The extraction of all k-mers and their frequencies is extremely demanding in terms of running time and memory, owing to the size of the data and to the exponential number of k-mers to be considered. However, in several applications, only frequent k-mers, which are k-mers appearing in a relatively high proportion of the data, are required by the analysis.

RESULTS

In this work, we present SPRISS, a new efficient algorithm to approximate frequent k-mers and their frequencies in next-generation sequencing data. SPRISS uses a simple yet powerful reads sampling scheme, which allows to extract a representative subset of the dataset that can be used, in combination with any k-mer counting algorithm, to perform downstream analyses in a fraction of the time required by the analysis of the whole data, while obtaining comparable answers. Our extensive experimental evaluation demonstrates the efficiency and accuracy of SPRISS in approximating frequent k-mers, and shows that it can be used in various scenarios, such as the comparison of metagenomic datasets, the identification of discriminative k-mers, and SNP (single nucleotide polymorphism) genotyping, to extract insights in a fraction of the time required by the analysis of the whole dataset.

AVAILABILITY AND IMPLEMENTATION

SPRISS [a preliminary version (Santoro et al., 2021) of this work was presented at RECOMB 2021] is available at https://github.com/VandinLab/SPRISS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Repetitive Sequence Barcode Probe for Karyotype Analysis in Tripidium arundinaceum

The barcode probe is a convenient and efficient tool for molecular cytogenetics. Tripidium arundinaceum, as a polyploid wild allied genus of Saccharum, is a useful genetic resource that confers biotic and abiotic stress resistance for sugarcane breeding. Unfortunately, the basic cytogenetic information is still unclear due to the complex genome. We constructed the Cot-20 library for screening moderately and highly repetitive sequences from T. arundinaceum, and the chromosomal distribution of these repetitive sequences was explored. We used the barcode of repetitive sequence probes to distinguish the ten chromosome types of T. arundinaceum by fluorescence in situ hybridization (FISH) with Ea-0907, Ea-0098, and 45S rDNA. Furthermore, the distinction among homology chromosomes based on repetitive sequences was constructed in T. arundinaceum by the repeated FISH using the barcode probes including Ea-0663, Ea-0267, EaCent, 5S rDNA, Ea-0265, Ea-0070, and 45S rDNA. We combined these probes to distinguish 37 different chromosome types, suggesting that the repetitive sequences may have different distributions on homologous chromosomes of T. arundinaceum. In summary, this method provide a basis for the development of similar applications for cytogenetic analysis in other species.

Chromosome-level genome assembly of a regenerable maize inbred line A188

BACKGROUND

The maize inbred line A188 is an attractive model for elucidation of gene function and improvement due to its high embryogenic capacity and many contrasting traits to the first maize reference genome, B73, and other elite lines. The lack of a genome assembly of A188 limits its use as a model for functional studies.

RESULTS

Here, we present a chromosome-level genome assembly of A188 using long reads and optical maps. Comparison of A188 with B73 using both whole-genome alignments and read depths from sequencing reads identify approximately 1.1 Gb of syntenic sequences as well as extensive structural variation, including a 1.8-Mb duplication containing the Gametophyte factor1 locus for unilateral cross-incompatibility, and six inversions of 0.7 Mb or greater. Increased copy number of carotenoid cleavage dioxygenase 1 (ccd1) in A188 is associated with elevated expression during seed development. High ccd1 expression in seeds together with low expression of yellow endosperm 1 (y1) reduces carotenoid accumulation, accounting for the white seed phenotype of A188. Furthermore, transcriptome and epigenome analyses reveal enhanced expression of defense pathways and altered DNA methylation patterns of the embryonic callus.

CONCLUSIONS

The A188 genome assembly provides a high-resolution sequence for a complex genome species and a foundational resource for analyses of genome variation and gene function in maize. The genome, in comparison to B73, contains extensive intra-species structural variations and other genetic differences. Expression and network analyses identify discrete profiles for embryonic callus and other tissues.

Factorial estimating assembly base errors using k-mer abundance difference (KAD) between short reads and genome assembled sequences

Genome sequences provide genomic maps with a single-base resolution for exploring genetic contents. Sequencing technologies, particularly long reads, have revolutionized genome assemblies for producing highly continuous genome sequences. However, current long-read sequencing technologies generate inaccurate reads that contain many errors. Some errors are retained in assembled sequences, which are typically not completely corrected by using either long reads or more accurate short reads. The issue commonly exists, but few tools are dedicated for computing error rates or determining error locations. In this study, we developed a novel approach, referred to as k-mer abundance difference (KAD), to compare the inferred copy number of each k-mer indicated by short reads and the observed copy number in the assembly. Simple KAD metrics enable to classify k-mers into categories that reflect the quality of the assembly. Specifically, the KAD method can be used to identify base errors and estimate the overall error rate. In addition, sequence insertion and deletion as well as sequence redundancy can also be detected. Collectively, KAD is valuable for quality evaluation of genome assemblies and, potentially, provides a diagnostic tool to aid in precise error correction. KAD software has been developed to facilitate public uses.

Kmasker plants - a tool for assessing complex sequence space in plant species

Many plant genomes display high levels of repetitive sequences. The assembly of these complex genomes using short high-throughput sequence reads is still a challenging task. Underestimation or disregard of repeat complexity in these datasets can easily misguide downstream analysis. Detection of repetitive regions by k-mer counting methods has proved to be reliable. Easy-to-use applications utilizing k-mer counting are in high demand, especially in the domain of plants. We present Kmasker plants, a tool that uses k-mer count information as an assistant throughout the analytical workflow of genome data that is provided as a command-line and web-based solution. Beside its core competence to screen and mask repetitive sequences, we have integrated features that enable comparative studies between different cultivars or closely related species and methods that estimate target specificity of guide RNAs for application of site-directed mutagenesis using Cas9 endonuclease. In addition, we have set up a web service for Kmasker plants that maintains pre-computed indices for 10 of the economically most important cultivated plants. Source code for Kmasker plants has been made publically available at https://github.com/tschmutzer/kmasker. The web service is accessible at https://kmasker.ipk-gatersleben.de.

On the Close Relatedness of Two Rice-Parasitic Root-Knot Nematode Species and the Recent Expansion of Meloidogyne graminicola in Southeast Asia

Meloidogyne graminicola is a facultative meiotic parthenogenetic root-knot nematode (RKN) that seriously threatens agriculture worldwide. We have little understanding of its origin, genomic structure, and intraspecific diversity. Such information would offer better knowledge of how this nematode successfully damages rice in many different environments. Previous studies on nuclear ribosomal DNA (nrDNA) suggested a close phylogenetic relationship between M. graminicola and Meloidogyne oryzae, despite their different modes of reproduction and geographical distribution. In order to clarify the evolutionary history of these two species and explore their molecular intraspecific diversity, we sequenced the genome of 12 M. graminicola isolates, representing populations of worldwide origins, and two South American isolates of M. oryzae. k-mer analysis of their nuclear genome and the detection of divergent homologous genomic sequences indicate that both species show a high proportion of heterozygous sites (ca. 1–2%), which had never been previously reported in facultative meiotic parthenogenetic RKNs. These analyses also point to a distinct ploidy level in each species, compatible with a diploid M. graminicola and a triploid M. oryzae. Phylogenetic analyses of mitochondrial genomes and three nuclear genomic sequences confirm close relationships between these two species, with M. graminicola being a putative parent of M. oryzae. In addition, comparative mitogenomics of those 12 M. graminicola isolates with a Chinese published isolate reveal only 15 polymorphisms that are phylogenetically non-informative. Eight mitotypes are distinguished, the most common one being shared by distant populations from Asia and America. This low intraspecific diversity, coupled with a lack of phylogeographic signal, suggests a recent worldwide expansion of M. graminicola.

Generating a high-confidence reference genome map of the Greater Duckweed by integration of cytogenomic, optical mapping, and Oxford Nanopore technologies

Duckweeds are the fastest growing angiosperms and have the potential to become a new generation of sustainable crops. Although a seed plant, Spirodela polyrhiza clones rarely flower and multiply mainly through vegetative propagation. Whole-genome sequencing using different approaches and clones yielded two reference maps. One for clone 9509, supported in its assembly by optical mapping of single DNA molecules, and one for clone 7498, supported by cytogenetic assignment of 96 fingerprinted bacterial artificial chromosomes (BACs) to its 20 chromosomes. However, these maps differ in the composition of several individual chromosome models. We validated both maps further to resolve these differences and addressed whether they could be due to chromosome rearrangements in different clones. For this purpose, we applied sequential multicolor fluorescence in situ hybridization (mcFISH) to seven S. polyrhiza clones, using 106 BACs that were mapped onto the 39 pseudomolecules for clone 7498. Furthermore we integrated high-depth Oxford Nanopore (ON) sequence data for clone 9509 to validate and revise the previously assembled chromosome models. We found no major structural rearrangements between these seven clones, identified seven chimeric pseudomolecules and Illumina assembly errors in the previous maps, respectively. A new S. polyrhiza genome map with high contiguity was produced with the ON sequence data and genome-wide synteny analysis supported the occurrence of two Whole Genome Duplication events during its evolution. This work generated a high confidence genome map for S. polyrhiza at the chromosome scale, and illustrates the complementarity of independent approaches to produce whole-genome assemblies in the absence of a genetic map.

Analysis of Extreme Phenotype Bulk Copy Number Variation (XP-CNV) Identified the Association of rp1 with Resistance to Goss's Wilt of Maize

Goss's wilt (GW) of maize is caused by the Gram-positive bacterium Clavibacter michiganensis subsp. nebraskensis (Cmn) and has spread in recent years throughout the Great Plains, posing a threat to production. The genetic basis of plant resistance is unknown. Here, a simple method for quantifying disease symptoms was developed and used to select cohorts of highly resistant and highly susceptible lines known as extreme phenotypes (XP). Copy number variation (CNV) analyses using whole genome sequences of bulked XP revealed 141 genes containing CNV between the two XP groups. The CNV genes include the previously identified common rust resistant locus rp1. Multiple Rp1 accessions with distinct rp1 haplotypes in an otherwise susceptible accession exhibited hypersensitive responses upon inoculation. GW provides an excellent system for the genetic dissection of diseases caused by closely related subspecies of C. michiganesis. Further work will facilitate breeding strategies to control GW and provide needed insight into the resistance mechanism of important related diseases such as bacterial canker of tomato and bacterial ring rot of potato.